Introduction to

Social Network Analysis

A 2-Day Seminar Taught by James Moody, Ph.D

Read reviews of this course

This seminar introduces the basic concepts and procedures of social network analysis, tracing the history of the field and covering the main topics relevant to the social sciences. The seminar is divided roughly into four parts based on two dimensions: deterministic vs. stochastic and network description vs. network effects. The four cells of this intersection take us from traditional descriptive characterizations of small complete networks to stochastic dynamic models of peer effects, roughly mirroring developments in the field over the last 30 years.

We’ll begin with a discussion of network data collection and the challenges and trade-offs of using data from different sources and formats. We then move to the measurement of structural characteristics on complete networks in a descriptive mode, distinguishing between features related to network composition (how types of people are connected) and network structure (mapping the topology of social life). Network structural features include centrality, social cohesion, clustering and hierarchy. Continuing in the deterministic mode, we consider how networks shape diffusion, identifying how topological and timing features affect flow over the network. This line of work has constituted the bulk of network modeling since the 1970s.

Recent advances in network modeling have shifted from a descriptive/inductive mode to a stochastic/hypothesis testing mode. The first major advance has been rapid growth in stochastic models of network structure embedded in the Exponential Random Graph Model (ERGM). These models allow us to integrate structural and compositional features of actor behavior to explain the sources of network topology. Recent work has attempted to make inference about peer influence more rigorous in ways that let us move beyond simple peer association models (network autocorrelation models) toward disentangling causality from selection. Among these models, the SiENA framework for dynamic network-behavior represents the state of the art.

This course will provide an overview of all of these topics, with a selection of hands-on examples on the second day. 

Who should attend?                        

This seminar is targeted at researchers from the social and behavioral sciences and medicine who are interested in social network analysis. Familiarity with SAS and access to the freeware package PAJEK will be needed for the hands-on material on the second day.

Schedule and materials

The class will meet from 9 to 4 each day with a 1-hour lunch break. 

Participants receive a bound manual containing detailed lecture notes (with equations and graphics), examples of computer printout, and many other useful features. This book frees participants from the distracting task of note taking. 

Registration and Lodging

The fee of $895 includes all course materials.  

Lodging Reservation Instructions

A block of rooms has been reserved at the Club Quarters Hotel, 1628 Chestnut St., Philadelphia, PA at a nightly rate of $137 for a Club room and $127 for a Standard room. This hotel is about a 5-minute walk from the seminar location.  To register, you must call 203-905-2100 during business hours and identify yourself with University of Pennsylvania and give the group code STA131. For guaranteed rate and availability, you must make your reservation by October 3, 2013. 


1. Introduction
2. Social Network data
       Basic data elements
       Network data sources
3. Local Network Models
       Network Composition
       Network Structure
       Local Network Models
4. Complete Network Analysis
       Exploratory Analysis
       Network Connections
       Network Macro Structure
       Stochastic Network Analyses
5.  Social Network Software Review
6.  Work through examples


“This course was a great introduction to the field. I walked away with new ideas to apply to my research and inspiration on how to approach my current data. Well worth the cost.”
     Vince Formica, Swarthmore College

“Great course. Really good upfront introduction to all concepts and brief introduction to software”
     Shweta Gaonkar, University of Maryland

“I found this class to be an excellent overview of SNA. It gave the ground sweeps of the subject matter, access disciplines which have contributed to the literature, from the earliest to the present. I was particularly impressed by Dr. Moody’s knowledge of research and publications, Dr. Moody being a prominent contributor, which will save me a great deal of time in further reading. The qualitative assessment of the available software was insightful.”
     William Serad, Kantar Health 

“The instructor and course material were excellent. I left with a solid understanding of ego-centric/local networks and a strong introduction to whole networks, as well as helpful information about the pros and cons of available analyses programs. I have great take-home resources to refer to as I continue to use SNA, as well as a small group of people who I can contact with questions to brainstorm with in the future.”
     Keli Steuber, University of Iowa

“Lectures are clear, to-the-point. Because they address a wide range of fields, lectures allow stepping back from one’s field of specialization and seeing the big picture. It is also inspiring to see how some problems that one wrestles in their own field are tackled in other fields.”
  Basak Taraktas, University of Pennsylvania